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      "name": "spatial_transformer_tutorial.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Eurus-Holmes/PyTorch-Tutorials/blob/master/spatial_transformer_tutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cvrFXVRxEDkM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mi2PAjRsEDkQ",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "Spatial Transformer Networks Tutorial\n",
        "=====================================\n",
        "**Author**: `Ghassen HAMROUNI <https://github.com/GHamrouni>`_\n",
        "\n",
        ".. figure:: /_static/img/stn/FSeq.png\n",
        "\n",
        "In this tutorial, you will learn how to augment your network using\n",
        "a visual attention mechanism called spatial transformer\n",
        "networks. You can read more about the spatial transformer\n",
        "networks in the `DeepMind paper <https://arxiv.org/abs/1506.02025>`__\n",
        "\n",
        "Spatial transformer networks are a generalization of differentiable\n",
        "attention to any spatial transformation. Spatial transformer networks\n",
        "(STN for short) allow a neural network to learn how to perform spatial\n",
        "transformations on the input image in order to enhance the geometric\n",
        "invariance of the model.\n",
        "For example, it can crop a region of interest, scale and correct\n",
        "the orientation of an image. It can be a useful mechanism because CNNs\n",
        "are not invariant to rotation and scale and more general affine\n",
        "transformations.\n",
        "\n",
        "One of the best things about STN is the ability to simply plug it into\n",
        "any existing CNN with very little modification.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OjuiWfqgEDkR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# License: BSD\n",
        "# Author: Ghassen Hamrouni\n",
        "\n",
        "from __future__ import print_function\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "import torchvision\n",
        "from torchvision import datasets, transforms\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "plt.ion()   # interactive mode"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pNL8rcFUEDkT",
        "colab_type": "text"
      },
      "source": [
        "Loading the data\n",
        "----------------\n",
        "\n",
        "In this post we experiment with the classic MNIST dataset. Using a\n",
        "standard convolutional network augmented with a spatial transformer\n",
        "network.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RCBJ1LdxEDkU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# Training dataset\n",
        "train_loader = torch.utils.data.DataLoader(\n",
        "    datasets.MNIST(root='.', train=True, download=True,\n",
        "                   transform=transforms.Compose([\n",
        "                       transforms.ToTensor(),\n",
        "                       transforms.Normalize((0.1307,), (0.3081,))\n",
        "                   ])), batch_size=64, shuffle=True, num_workers=4)\n",
        "# Test dataset\n",
        "test_loader = torch.utils.data.DataLoader(\n",
        "    datasets.MNIST(root='.', train=False, transform=transforms.Compose([\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize((0.1307,), (0.3081,))\n",
        "    ])), batch_size=64, shuffle=True, num_workers=4)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j5ubzJ1fEDkV",
        "colab_type": "text"
      },
      "source": [
        "Depicting spatial transformer networks\n",
        "--------------------------------------\n",
        "\n",
        "Spatial transformer networks boils down to three main components :\n",
        "\n",
        "-  The localization network is a regular CNN which regresses the\n",
        "   transformation parameters. The transformation is never learned\n",
        "   explicitly from this dataset, instead the network learns automatically\n",
        "   the spatial transformations that enhances the global accuracy.\n",
        "-  The grid generator generates a grid of coordinates in the input\n",
        "   image corresponding to each pixel from the output image.\n",
        "-  The sampler uses the parameters of the transformation and applies\n",
        "   it to the input image.\n",
        "\n",
        ".. figure:: /_static/img/stn/stn-arch.png\n",
        "\n",
        ".. Note::\n",
        "   We need the latest version of PyTorch that contains\n",
        "   affine_grid and grid_sample modules.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FgI0PmGQEDkW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n",
        "        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n",
        "        self.conv2_drop = nn.Dropout2d()\n",
        "        self.fc1 = nn.Linear(320, 50)\n",
        "        self.fc2 = nn.Linear(50, 10)\n",
        "\n",
        "        # Spatial transformer localization-network\n",
        "        self.localization = nn.Sequential(\n",
        "            nn.Conv2d(1, 8, kernel_size=7),\n",
        "            nn.MaxPool2d(2, stride=2),\n",
        "            nn.ReLU(True),\n",
        "            nn.Conv2d(8, 10, kernel_size=5),\n",
        "            nn.MaxPool2d(2, stride=2),\n",
        "            nn.ReLU(True)\n",
        "        )\n",
        "\n",
        "        # Regressor for the 3 * 2 affine matrix\n",
        "        self.fc_loc = nn.Sequential(\n",
        "            nn.Linear(10 * 3 * 3, 32),\n",
        "            nn.ReLU(True),\n",
        "            nn.Linear(32, 3 * 2)\n",
        "        )\n",
        "\n",
        "        # Initialize the weights/bias with identity transformation\n",
        "        self.fc_loc[2].weight.data.zero_()\n",
        "        self.fc_loc[2].bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0], dtype=torch.float))\n",
        "\n",
        "    # Spatial transformer network forward function\n",
        "    def stn(self, x):\n",
        "        xs = self.localization(x)\n",
        "        xs = xs.view(-1, 10 * 3 * 3)\n",
        "        theta = self.fc_loc(xs)\n",
        "        theta = theta.view(-1, 2, 3)\n",
        "\n",
        "        grid = F.affine_grid(theta, x.size())\n",
        "        x = F.grid_sample(x, grid)\n",
        "\n",
        "        return x\n",
        "\n",
        "    def forward(self, x):\n",
        "        # transform the input\n",
        "        x = self.stn(x)\n",
        "\n",
        "        # Perform the usual forward pass\n",
        "        x = F.relu(F.max_pool2d(self.conv1(x), 2))\n",
        "        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n",
        "        x = x.view(-1, 320)\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.dropout(x, training=self.training)\n",
        "        x = self.fc2(x)\n",
        "        return F.log_softmax(x, dim=1)\n",
        "\n",
        "\n",
        "model = Net().to(device)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OlL52nM6EDkY",
        "colab_type": "text"
      },
      "source": [
        "Training the model\n",
        "------------------\n",
        "\n",
        "Now, let's use the SGD algorithm to train the model. The network is\n",
        "learning the classification task in a supervised way. In the same time\n",
        "the model is learning STN automatically in an end-to-end fashion.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xM9a62jhEDkZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "optimizer = optim.SGD(model.parameters(), lr=0.01)\n",
        "\n",
        "\n",
        "def train(epoch):\n",
        "    model.train()\n",
        "    for batch_idx, (data, target) in enumerate(train_loader):\n",
        "        data, target = data.to(device), target.to(device)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        output = model(data)\n",
        "        loss = F.nll_loss(output, target)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        if batch_idx % 500 == 0:\n",
        "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
        "                epoch, batch_idx * len(data), len(train_loader.dataset),\n",
        "                100. * batch_idx / len(train_loader), loss.item()))\n",
        "#\n",
        "# A simple test procedure to measure STN the performances on MNIST.\n",
        "#\n",
        "\n",
        "\n",
        "def test():\n",
        "    with torch.no_grad():\n",
        "        model.eval()\n",
        "        test_loss = 0\n",
        "        correct = 0\n",
        "        for data, target in test_loader:\n",
        "            data, target = data.to(device), target.to(device)\n",
        "            output = model(data)\n",
        "\n",
        "            # sum up batch loss\n",
        "            test_loss += F.nll_loss(output, target, size_average=False).item()\n",
        "            # get the index of the max log-probability\n",
        "            pred = output.max(1, keepdim=True)[1]\n",
        "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
        "\n",
        "        test_loss /= len(test_loader.dataset)\n",
        "        print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'\n",
        "              .format(test_loss, correct, len(test_loader.dataset),\n",
        "                      100. * correct / len(test_loader.dataset)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7VOvM8ZZEDkc",
        "colab_type": "text"
      },
      "source": [
        "Visualizing the STN results\n",
        "---------------------------\n",
        "\n",
        "Now, we will inspect the results of our learned visual attention\n",
        "mechanism.\n",
        "\n",
        "We define a small helper function in order to visualize the\n",
        "transformations while training.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "INWPrQ1uEDkc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1970
        },
        "outputId": "9e17bcfb-f52a-4a57-d98f-c2f16589a7e8"
      },
      "source": [
        "def convert_image_np(inp):\n",
        "    \"\"\"Convert a Tensor to numpy image.\"\"\"\n",
        "    inp = inp.numpy().transpose((1, 2, 0))\n",
        "    mean = np.array([0.485, 0.456, 0.406])\n",
        "    std = np.array([0.229, 0.224, 0.225])\n",
        "    inp = std * inp + mean\n",
        "    inp = np.clip(inp, 0, 1)\n",
        "    return inp\n",
        "\n",
        "# We want to visualize the output of the spatial transformers layer\n",
        "# after the training, we visualize a batch of input images and\n",
        "# the corresponding transformed batch using STN.\n",
        "\n",
        "\n",
        "def visualize_stn():\n",
        "    with torch.no_grad():\n",
        "        # Get a batch of training data\n",
        "        data = next(iter(test_loader))[0].to(device)\n",
        "\n",
        "        input_tensor = data.cpu()\n",
        "        transformed_input_tensor = model.stn(data).cpu()\n",
        "\n",
        "        in_grid = convert_image_np(\n",
        "            torchvision.utils.make_grid(input_tensor))\n",
        "\n",
        "        out_grid = convert_image_np(\n",
        "            torchvision.utils.make_grid(transformed_input_tensor))\n",
        "\n",
        "        # Plot the results side-by-side\n",
        "        f, axarr = plt.subplots(1, 2)\n",
        "        axarr[0].imshow(in_grid)\n",
        "        axarr[0].set_title('Dataset Images')\n",
        "\n",
        "        axarr[1].imshow(out_grid)\n",
        "        axarr[1].set_title('Transformed Images')\n",
        "\n",
        "\n",
        "for epoch in range(1, 20 + 1):\n",
        "    train(epoch)\n",
        "    test()\n",
        "\n",
        "# Visualize the STN transformation on some input batch\n",
        "visualize_stn()\n",
        "\n",
        "plt.ioff()\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Epoch: 1 [0/60000 (0%)]\tLoss: 2.323041\n",
            "Train Epoch: 1 [32000/60000 (53%)]\tLoss: 1.070700\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/_reduction.py:46: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.\n",
            "  warnings.warn(warning.format(ret))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.2228, Accuracy: 9380/10000 (94%)\n",
            "\n",
            "Train Epoch: 2 [0/60000 (0%)]\tLoss: 0.496635\n",
            "Train Epoch: 2 [32000/60000 (53%)]\tLoss: 0.202981\n",
            "\n",
            "Test set: Average loss: 0.2318, Accuracy: 9266/10000 (93%)\n",
            "\n",
            "Train Epoch: 3 [0/60000 (0%)]\tLoss: 0.422035\n",
            "Train Epoch: 3 [32000/60000 (53%)]\tLoss: 0.381551\n",
            "\n",
            "Test set: Average loss: 0.0967, Accuracy: 9710/10000 (97%)\n",
            "\n",
            "Train Epoch: 4 [0/60000 (0%)]\tLoss: 0.041278\n",
            "Train Epoch: 4 [32000/60000 (53%)]\tLoss: 0.175728\n",
            "\n",
            "Test set: Average loss: 0.1172, Accuracy: 9659/10000 (97%)\n",
            "\n",
            "Train Epoch: 5 [0/60000 (0%)]\tLoss: 0.350597\n",
            "Train Epoch: 5 [32000/60000 (53%)]\tLoss: 0.498956\n",
            "\n",
            "Test set: Average loss: 0.0979, Accuracy: 9710/10000 (97%)\n",
            "\n",
            "Train Epoch: 6 [0/60000 (0%)]\tLoss: 0.199039\n",
            "Train Epoch: 6 [32000/60000 (53%)]\tLoss: 0.208250\n",
            "\n",
            "Test set: Average loss: 0.0621, Accuracy: 9810/10000 (98%)\n",
            "\n",
            "Train Epoch: 7 [0/60000 (0%)]\tLoss: 0.367445\n",
            "Train Epoch: 7 [32000/60000 (53%)]\tLoss: 0.212987\n",
            "\n",
            "Test set: Average loss: 0.0603, Accuracy: 9816/10000 (98%)\n",
            "\n",
            "Train Epoch: 8 [0/60000 (0%)]\tLoss: 0.238150\n",
            "Train Epoch: 8 [32000/60000 (53%)]\tLoss: 0.105397\n",
            "\n",
            "Test set: Average loss: 0.0653, Accuracy: 9812/10000 (98%)\n",
            "\n",
            "Train Epoch: 9 [0/60000 (0%)]\tLoss: 0.248694\n",
            "Train Epoch: 9 [32000/60000 (53%)]\tLoss: 0.086615\n",
            "\n",
            "Test set: Average loss: 0.0536, Accuracy: 9825/10000 (98%)\n",
            "\n",
            "Train Epoch: 10 [0/60000 (0%)]\tLoss: 0.128454\n",
            "Train Epoch: 10 [32000/60000 (53%)]\tLoss: 0.153521\n",
            "\n",
            "Test set: Average loss: 0.0526, Accuracy: 9841/10000 (98%)\n",
            "\n",
            "Train Epoch: 11 [0/60000 (0%)]\tLoss: 0.131456\n",
            "Train Epoch: 11 [32000/60000 (53%)]\tLoss: 0.080585\n",
            "\n",
            "Test set: Average loss: 0.0480, Accuracy: 9863/10000 (99%)\n",
            "\n",
            "Train Epoch: 12 [0/60000 (0%)]\tLoss: 0.066106\n",
            "Train Epoch: 12 [32000/60000 (53%)]\tLoss: 0.204523\n",
            "\n",
            "Test set: Average loss: 0.0507, Accuracy: 9848/10000 (98%)\n",
            "\n",
            "Train Epoch: 13 [0/60000 (0%)]\tLoss: 0.102243\n",
            "Train Epoch: 13 [32000/60000 (53%)]\tLoss: 0.038294\n",
            "\n",
            "Test set: Average loss: 0.0543, Accuracy: 9850/10000 (98%)\n",
            "\n",
            "Train Epoch: 14 [0/60000 (0%)]\tLoss: 0.109191\n",
            "Train Epoch: 14 [32000/60000 (53%)]\tLoss: 0.034303\n",
            "\n",
            "Test set: Average loss: 0.0656, Accuracy: 9794/10000 (98%)\n",
            "\n",
            "Train Epoch: 15 [0/60000 (0%)]\tLoss: 0.259922\n",
            "Train Epoch: 15 [32000/60000 (53%)]\tLoss: 0.116720\n",
            "\n",
            "Test set: Average loss: 0.0451, Accuracy: 9864/10000 (99%)\n",
            "\n",
            "Train Epoch: 16 [0/60000 (0%)]\tLoss: 0.043364\n",
            "Train Epoch: 16 [32000/60000 (53%)]\tLoss: 0.067063\n",
            "\n",
            "Test set: Average loss: 0.0638, Accuracy: 9828/10000 (98%)\n",
            "\n",
            "Train Epoch: 17 [0/60000 (0%)]\tLoss: 0.179573\n",
            "Train Epoch: 17 [32000/60000 (53%)]\tLoss: 0.057824\n",
            "\n",
            "Test set: Average loss: 0.0465, Accuracy: 9863/10000 (99%)\n",
            "\n",
            "Train Epoch: 18 [0/60000 (0%)]\tLoss: 0.035433\n",
            "Train Epoch: 18 [32000/60000 (53%)]\tLoss: 0.144730\n",
            "\n",
            "Test set: Average loss: 0.0507, Accuracy: 9861/10000 (99%)\n",
            "\n",
            "Train Epoch: 19 [0/60000 (0%)]\tLoss: 0.199269\n",
            "Train Epoch: 19 [32000/60000 (53%)]\tLoss: 0.136273\n",
            "\n",
            "Test set: Average loss: 0.0448, Accuracy: 9875/10000 (99%)\n",
            "\n",
            "Train Epoch: 20 [0/60000 (0%)]\tLoss: 0.026595\n",
            "Train Epoch: 20 [32000/60000 (53%)]\tLoss: 0.027877\n",
            "\n",
            "Test set: Average loss: 0.0482, Accuracy: 9865/10000 (99%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RvQE5RkvJxLNxRrg4O5r34jpnz53lnrYZnD129HZXpUyglyRSUlNZtGSJcZst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\nzdtj9kXhb9XNb7/+WiJCGLcaDRs0IKJUDJDD2XPnipWILRU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Gm6Hd7GzXnVn/pG4kxMcT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VrNtcOGERrbAakkh4sXq0ah1fnYNWoN4yZOsv39xrvvgtADTgau4gKH/jjkMeWtlSGD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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}